Supplemental learning resources
To effectively utilize these materials, the reader need not be a fully trained
statistician. However, it is highly recommended for the reader to have an
understanding of basic statistical concepts such as confounding, probability
distributions, confidence intervals, hypothesis tests, and regression. Advanced
knowledge of mathematical statistics may be useful but is not necessary.
Familiarity with the R
programming language will be essential. We also
recommend an understanding of introductory causal inference.
For learning the R
programming language we recommend the following (free)
introductory resources:
- Software Carpentry’s Programming with
R
- Software Carpentry’s
R
for Reproducible Scientific Analysis - Garret Grolemund and Hadley Wickham’s
R
for Data Science
For a general, modern introduction to causal inference, we recommend
- Miguel A. Hernán and James M. Robins’ Causal Inference: What If (2022)
- Jason A. Roy’s A Crash Course in Causality: Inferring Causal Effects from Observational Data on Coursera
Feel free to suggest a resource!